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Will China's building sector participate in emission trading system? Insights from modelling an owner's optimal carbon reduction strategies

Abstract Building sector is a significant contributor to the global warming and thus the control of carbon emissions from buildings has received unprecedented attention. While China is pioneering in including building sector in its Emission Trading System (ETS) pilots, there is few practical trading. This study investigates the reasons of lack trading via exploring a building owner's optimal strategy that is based on a multi-objective optimization model to achieve required carbon emissions reduction with minimal incremental costs. The investigated emissions reduction strategies include adopting low-carbon technologies, purchasing emission permits from ETS market, and non-compliance. A typical four-star hotel in Shenzhen, China is selected as an empirical case to validate the proposed model. The result shows that non-compliance is the preferable strategy by the owners, and there is no permits trading from the carbon market. Key influencing factors that affect the owners’ strategic choice are further investigated with various scenarios and it is found that the probability of government environmental inspection, the penalty for non-compliance, and an owner's reputation loss will to a large extent change an owner's strategy. These findings provide a quantitative rationale for policymakers to reformulate existing initiatives and mechanisms to invigorate the ETS market in the building sector.
- National University of Singapore Singapore
- Guizhou University China (People's Republic of)
- University of Technology Sydney Australia
- Guangzhou University China (People's Republic of)
- Chongqing University China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).54 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
